Explarartory Questions:

Properties and Pricing

  1. How do neighborhoods differ in price?
  2. Does proximity to attractions play a factor in price?
  3. What kind of properties are these listings (property type, bedroom count, bathroom count, etc.)
  • Host "business" analysis: Are there hosts that have multiple listings/have businesses using Aribnb? Do these hosts have good scores and reviews? Do they tend to be in certain neighbourhoods and have listings with certain prices?
  • Where and maybe which host should I prefer to stay at/with to increase my changes of having a good experience? In both cheap and expensive neighbourhoods?
  • </ol>

    In [1]:
    # imports 
    import numpy as np
    import pandas as pd
    import matplotlib.pyplot as plt
    import plotly.express as px
    import seaborn as sns
    import json
    import ipywidgets as widgets
    import plotly
    %matplotlib inline
    
    # my favorite
    plt.style.use("fivethirtyeight")
    
    # show full columns
    pd.set_option('display.max_columns', None)
    
    # cell width 
    from IPython.core.display import display, HTML
    display(HTML("<style>.container { width:80% !important; }</style>"))
    

    Datasets available to us: listings, reviews, geographical information

    In [2]:
    # listings data
    ls = pd.read_csv("../data/listings.csv")
    ls_d = pd.read_csv("../data/listings 2.csv")
    
    # reviews data
    rs = pd.read_csv("../data/reviews.csv")
    rs_d = pd.read_csv("../data/reviews 2.csv")
    
    
    # geography data
    geo = pd.read_csv("../data/neighbourhoods.csv")
    
    with open("../data/neighbourhoods.geojson") as jsonfile:
        geojson = json.load(jsonfile)
    
    /Users/ldugom/.local/lib/python3.6/site-packages/IPython/core/interactiveshell.py:3063: DtypeWarning:
    
    Columns (61,62,94) have mixed types.Specify dtype option on import or set low_memory=False.
    
    

    Preliminary: What/Where are the neighbourhoods in DC?

    In [140]:
    import plotly.express as px
    
    # set token
    px.set_mapbox_access_token("pk.eyJ1IjoibGF3cmVuY2VkIiwiYSI6ImNrODFzZnFnNzA0YmczZW9nNWN4aTFvdngifQ.VlB5-L7owXKEXo8JEePk7w")
    fig = px.choropleth_mapbox(ls, geojson=geojson, color="neighbourhood", title="Washington D.C. Neighbourhood Map",
                               locations="neighbourhood", featureidkey="properties.neighbourhood",opacity=0.5, color_discrete_sequence=px.colors.qualitative.Light24,
                               center={"lat": 38.9072, "lon": -77.0369},
                               mapbox_style="light", zoom=11)
    
    fig.update_layout(margin={"r":0,"t":0,"l":0,"b":0})
    fig.show()
    
    In [141]:
    ls_d['price'].corr(ls_d['bedrooms'])
    
    Out[141]:
    0.2036136776639811

    Explore numerical attributes

    In [3]:
    # melt listings data for graphing purposes 
    melted_ls = pd.melt(ls,id_vars=['id', 'name', 'host_id', 'host_name', 'neighbourhood_group','neighbourhood', 'latitude', 'longitude'], value_vars=ls.select_dtypes(include=np.number).iloc[:, 5:].columns.tolist())
    
    In [4]:
    # create dropdown for attributes in melted dataframe
    dropdown_attribute = widgets.Dropdown(options = sorted(melted_ls.variable.unique()))
    
    # output
    output = widgets.Output()
    
    
    def view_attribute(attribute):
        
        # clear output for new attribute to be plotted 
        output.clear_output()
        
        # filter df to selected attribute
        filtered = melted_ls[melted_ls['variable'] == attribute].copy()
        
        # filter out outliers 
        filtered = filtered[filtered.value.between(filtered.value.quantile(.10), filtered.value.quantile(.80))]
     
        with output:
            # set token
            px.set_mapbox_access_token("pk.eyJ1IjoibGF3cmVuY2VkIiwiYSI6ImNrODFzZnFnNzA0YmczZW9nNWN4aTFvdngifQ.VlB5-L7owXKEXo8JEePk7w")
            fig = px.scatter_mapbox(filtered.rename({'value':'Listing Price'}, axis=1), lat="latitude", lon="longitude", color="Listing Price", template="gridon", 
                                    color_continuous_scale=plotly.colors.diverging.RdYlGn,
                                       opacity=0.4, center={"lat": 38.9072, "lon": -77.0369},
                                       mapbox_style="light", zoom=11)
    
            fig.update_layout(margin={"r":0,"t":0,"l":0,"b":0})
            fig.show()
    
    # change attribute
    def dropdown_attribute_handler(change):
        view_attribute(change.new)
        
    dropdown_attribute.observe(dropdown_attribute_handler, names="value")
    display(dropdown_attribute)
    display(output)
    

    How do neighborhoods differ in price?

    Are certain neighbourhoods generally more expensive than others?

    Does proximity to attractions play a factor in price?

    In [5]:
    # calculate outliers within neighbourhoods 
    stdev = 3.0
    
    zscores = ls[['neighbourhood', 'price']].groupby('neighbourhood').transform(
        lambda group: (group - group.mean()).div(group.std())).abs()
    
    outliers = zscores > stdev
    
    # take out outliers 
    ls_nonoutliers = ls[~outliers.any(axis=1)].copy()
    
    
    # agg all the observations by nieghbourhood to get median and mean 
    summary = ls.groupby("neighbourhood").agg({'price':['mean', 'median', 'count']}).reset_index()
    summary.columns = ['Neighbourhood', 'Mean Price', 'Median Price', '# of listings']
    
    # strip outliers and redo agg
    summary_nonoutliers = ls_nonoutliers.groupby("neighbourhood").agg({'price':['mean', 'count']}).reset_index()
    summary_nonoutliers.columns = ['Neighbourhood', 'Mean Price', '# of listings']
    
    # merge 
    summary_final = pd.merge(summary, summary_nonoutliers, on='Neighbourhood', suffixes=[" with outliers", " without outliers"])
    summary_final["# of outliers"] = summary_final["# of listings with outliers"] - summary_final["# of listings without outliers"] 
    summary_final.drop(['# of listings without outliers'], axis=1, inplace=True)
    
    # mean price by nieghborhood 
    (
        summary_final
        .sort_values(["Mean Price without outliers"], ascending=False)[['Neighbourhood', 'Mean Price with outliers', 'Mean Price without outliers', 'Median Price', '# of outliers']]
        .style.background_gradient(cmap='RdYlGn', subset=['Mean Price with outliers', 'Median Price', 'Mean Price without outliers']) 
        
              
    )
    
    Out[5]:
    Neighbourhood Mean Price with outliers Mean Price without outliers Median Price # of outliers
    36 West End, Foggy Bottom, GWU 304.280822 298.625430 150.000000 1
    11 Downtown, Chinatown, Penn Quarters, Mount Vernon Square, North Capitol Street 274.610526 247.212766 173.000000 4
    17 Georgetown, Burleith/Hillandale 296.938224 235.964844 162.000000 3
    31 Southwest Employment Area, Southwest/Waterfront, Fort McNair, Buzzard Point 270.191489 220.862637 189.500000 6
    29 Shaw, Logan Circle 290.917910 210.158850 144.500000 9
    32 Spring Valley, Palisades, Wesley Heights, Foxhall Crescent, Foxhall Village, Georgetown Reservoir 288.104651 209.250000 122.500000 2
    2 Capitol Hill, Lincoln Park 260.604317 207.454545 135.000000 9
    20 Howard University, Le Droit Park, Cardozo/Shaw 333.938719 201.375000 130.000000 7
    12 Dupont Circle, Connecticut Avenue/K Street 250.692416 195.617232 142.500000 4
    5 Cleveland Park, Woodley Park, Massachusetts Avenue Heights, Woodland-Normanstone Terrace 199.610619 179.964286 115.000000 1
    35 Union Station, Stanton Park, Kingman Park 220.582380 177.423212 125.000000 21
    25 Near Southeast, Navy Yard 177.555556 170.400000 131.000000 1
    4 Cathedral Heights, McLean Gardens, Glover Park 204.808824 164.541985 99.500000 5
    18 Hawthorne, Barnaby Woods, Chevy Chase 235.377049 164.389831 100.000000 2
    22 Kalorama Heights, Adams Morgan, Lanier Heights 174.108108 144.592500 119.000000 7
    26 North Cleveland Park, Forest Hills, Van Ness 198.028169 143.432836 110.000000 4
    14 Edgewood, Bloomingdale, Truxton Circle, Eckington 168.550484 137.110638 100.000000 18
    15 Fairfax Village, Naylor Gardens, Hillcrest, Summit Park 128.636364 128.636364 99.000000 0
    7 Columbia Heights, Mt. Pleasant, Pleasant Plains, Park View 148.452915 128.066514 99.000000 20
    8 Congress Heights, Bellevue, Washington Highlands 166.463415 126.250000 92.000000 2
    37 Woodland/Fort Stanton, Garfield Heights, Knox Hill 120.555556 120.555556 69.000000 0
    28 River Terrace, Benning, Greenway, Dupont Park 179.055556 120.169811 70.000000 1
    19 Historic Anacostia 143.360656 119.406780 75.000000 2
    0 Brightwood Park, Crestwood, Petworth 144.827977 118.123791 88.000000 12
    34 Twining, Fairlawn, Randle Highlands, Penn Branch, Fort Davis Park, Fort Dupont 140.739496 117.504274 80.000000 2
    16 Friendship Heights, American University Park, Tenleytown 136.488372 112.506024 99.000000 3
    21 Ivy City, Arboretum, Trinidad, Carver Langston 127.266917 110.702290 95.000000 4
    6 Colonial Village, Shepherd Park, North Portal Estates 127.975000 109.461538 86.000000 1
    10 Douglas, Shipley Terrace 143.709677 106.833333 65.000000 1
    23 Lamont Riggs, Queens Chapel, Fort Totten, Pleasant Hill 131.128205 102.086207 75.000000 1
    3 Capitol View, Marshall Heights, Benning Heights 118.040000 102.067568 70.000000 1
    1 Brookland, Brentwood, Langdon 118.192771 96.822086 79.500000 3
    30 Sheridan, Barry Farm, Buena Vista 136.478261 95.066667 72.000000 1
    38 Woodridge, Fort Lincoln, Gateway 111.846154 93.190476 80.000000 2
    27 North Michigan Park, Michigan Park, University Heights 110.239583 89.180851 75.000000 2
    33 Takoma, Brightwood, Manor Park 96.852041 85.927461 68.000000 3
    13 Eastland Gardens, Kenilworth 108.428571 83.307692 70.000000 1
    24 Mayfair, Hillbrook, Mahaning Heights 94.491525 83.189655 65.000000 1
    9 Deanwood, Burrville, Grant Park, Lincoln Heights, Fairmont Heights 92.765957 75.333333 75.000000 2
    In [6]:
    ls_nonoutliers['price'].hist(bins=50)
    
    Out[6]:
    <matplotlib.axes._subplots.AxesSubplot at 0x1a19a75400>
    In [7]:
    def format_x(x):
        return "${:.1f}K".format(float(x))
    
    
    t = (summary_final
        .sort_values(["Mean Price without outliers"], ascending=False)[['Neighbourhood', 'Mean Price without outliers']]
         .rename({'Mean Price without outliers':'Average Price'}, axis=1)
        )
    
    t['Average Price'] = t['Average Price'].apply(lambda x: round(x))
    
    t.style.background_gradient(cmap='RdYlGn', subset=['Average Price'])
    
    Out[7]:
    Neighbourhood Average Price
    36 West End, Foggy Bottom, GWU 299
    11 Downtown, Chinatown, Penn Quarters, Mount Vernon Square, North Capitol Street 247
    17 Georgetown, Burleith/Hillandale 236
    31 Southwest Employment Area, Southwest/Waterfront, Fort McNair, Buzzard Point 221
    29 Shaw, Logan Circle 210
    32 Spring Valley, Palisades, Wesley Heights, Foxhall Crescent, Foxhall Village, Georgetown Reservoir 209
    2 Capitol Hill, Lincoln Park 207
    20 Howard University, Le Droit Park, Cardozo/Shaw 201
    12 Dupont Circle, Connecticut Avenue/K Street 196
    5 Cleveland Park, Woodley Park, Massachusetts Avenue Heights, Woodland-Normanstone Terrace 180
    35 Union Station, Stanton Park, Kingman Park 177
    25 Near Southeast, Navy Yard 170
    4 Cathedral Heights, McLean Gardens, Glover Park 165
    18 Hawthorne, Barnaby Woods, Chevy Chase 164
    22 Kalorama Heights, Adams Morgan, Lanier Heights 145
    26 North Cleveland Park, Forest Hills, Van Ness 143
    14 Edgewood, Bloomingdale, Truxton Circle, Eckington 137
    15 Fairfax Village, Naylor Gardens, Hillcrest, Summit Park 129
    7 Columbia Heights, Mt. Pleasant, Pleasant Plains, Park View 128
    8 Congress Heights, Bellevue, Washington Highlands 126
    37 Woodland/Fort Stanton, Garfield Heights, Knox Hill 121
    28 River Terrace, Benning, Greenway, Dupont Park 120
    19 Historic Anacostia 119
    0 Brightwood Park, Crestwood, Petworth 118
    34 Twining, Fairlawn, Randle Highlands, Penn Branch, Fort Davis Park, Fort Dupont 118
    16 Friendship Heights, American University Park, Tenleytown 113
    21 Ivy City, Arboretum, Trinidad, Carver Langston 111
    6 Colonial Village, Shepherd Park, North Portal Estates 109
    10 Douglas, Shipley Terrace 107
    23 Lamont Riggs, Queens Chapel, Fort Totten, Pleasant Hill 102
    3 Capitol View, Marshall Heights, Benning Heights 102
    1 Brookland, Brentwood, Langdon 97
    30 Sheridan, Barry Farm, Buena Vista 95
    38 Woodridge, Fort Lincoln, Gateway 93
    27 North Michigan Park, Michigan Park, University Heights 89
    33 Takoma, Brightwood, Manor Park 86
    13 Eastland Gardens, Kenilworth 83
    24 Mayfair, Hillbrook, Mahaning Heights 83
    9 Deanwood, Burrville, Grant Park, Lincoln Heights, Fairmont Heights 75
    In [10]:
    # top and bottom twelve nieghborhoods
    most_expensive_neighbourhoods = summary_final.sort_values(["Mean Price without outliers"], ascending=False)[:12].Neighbourhood.values.tolist()
    cheapest_neighbourhoods= summary_final.sort_values(["Mean Price without outliers"])[:12].Neighbourhood.values.tolist()
    
    top = ls[ls.neighbourhood.isin(most_expensive_neighbourhoods + cheapest_neighbourhoods)]
    top['category'] = top['neighbourhood'].apply(lambda x: 'Top 5 Cheapest' if x in cheapest_neighbourhoods else 'Top 5 Most Expensive')
    
    
    # you save an average of 3x your money by choosing a nieghbourhood from the 5 cheapest rather than the 5 most expsensive 
    top.groupby("category").mean()['price'].reset_index().rename({'price':'Average price in Category'}, axis=1) 
    
    /Users/ldugom/.local/lib/python3.6/site-packages/ipykernel_launcher.py:6: SettingWithCopyWarning:
    
    
    A value is trying to be set on a copy of a slice from a DataFrame.
    Try using .loc[row_indexer,col_indexer] = value instead
    
    See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
    
    
    Out[10]:
    category Average price in Category
    0 Top 5 Cheapest 113.399160
    1 Top 5 Most Expensive 265.273309

    Looking at the 12 most expensive and 12 most inexpensive neighbourhoods visually (39 total)

    In [14]:
    px.set_mapbox_access_token("pk.eyJ1IjoibGF3cmVuY2VkIiwiYSI6ImNrODFzZnFnNzA0YmczZW9nNWN4aTFvdngifQ.VlB5-L7owXKEXo8JEePk7w")
    fig = px.scatter_mapbox(top, lat="latitude", lon="longitude", color="category", template="simple_white",center={"lat": 38.895, "lon": -77.024},
                               mapbox_style="basic", zoom=10)
    

    How expensive is the top 5%?

    Answer: We can see that top listings start at ~ \$600, with a few going all the way to $10,000 ! Georgetown, Capitol, Hill and Downtown/Chinatown seem to pop out the most.

    In [16]:
    # clean price 
    ls_d["price"] = ls_d["price"].str[1:].str.replace(",","").astype(float)
    
    # detect outliers (169 total)
    outliers_df = ls_d[ls.price.between(ls_d.price.quantile(.95), ls_d.price.quantile(1))].copy().dropna(subset=["beds"])
    
    sns.set_style("dark") 
    sns.set_palette("Set3")
    
    fig = px.scatter(outliers_df, y="neighbourhood", x="price", size="bedrooms", color="bedrooms", template="plotly_dark", color_continuous_scale="sunset", width=1500, height=1000)
    
    In [17]:
    plotly.io.orca.config.executable = '/Users/ldugom/anaconda3/envs/ds/bin/orca'
    fig.write_image("outliers.png", width=1500, height=1000)
    
    In [18]:
    ls_d['price'].corr(ls_d['beds'])
    
    Out[18]:
    0.1922988708297365

    From a glance, it looks like the more expensive listings tend to be closer to downtown, but can we assume the farther you move away from the center of DC, the more likely you are to find a cheaper listing?

    In [19]:
    # create bins to eliminate outliers from dominating continuous scale
    ls['Price Decile'] = pd.qcut(ls['price'], 10)
    ls_d['Price Decile'] = pd.qcut(ls_d['price'], 10)
    
    
    # set token
    px.set_mapbox_access_token("pk.eyJ1IjoibGF3cmVuY2VkIiwiYSI6ImNrODFzZnFnNzA0YmczZW9nNWN4aTFvdngifQ.VlB5-L7owXKEXo8JEePk7w")
    fig = px.scatter_mapbox(ls, lat="latitude", lon="longitude", color="Price Decile", template="simple_white", opacity=0.5, 
                             color_discrete_sequence=px.colors.diverging.RdYlGn, category_orders={"Price Decile":sorted(ls['Price Decile'].unique().tolist())}, hover_data=["neighbourhood", "price"],
                               center={"lat": 38.895, "lon": -77.024},
                               mapbox_style="basic", zoom=12)
    
    
    fig.update_layout(margin={"r":0,"t":0,"l":0,"b":0})
    
    In [75]:
    fig.write_image("binned_prices.png", height=850, width=1200)
    

    To test our hypothesis about distance to downtown/attractions and being in more of an expensive price range, we'll calculate the distnace to Capital One Arena in Downtown DC as well as the Washington Monument, which is at teh center of a lot of the DC attractions

    In [20]:
    monument = {'lat':38.8872036, 'lon':-77.045968}
    arena = {'lat':38.8980942,'lon':-77.0208438}
    center = {'lat':38.9072,'lon':-77.0369}
    wh = {'lat': 38.8977, 'lon':-77.0365}
    from math import radians, cos, sin, asin, sqrt
    
    
    # Thanks https://stackoverflow.com/questions/4913349/haversine-formula-in-python-bearing-and-distance-between-two-gps-points
        
        
    def haversine(lon1, lat1, lon2, lat2):
        """
        Calculate the great circle distance between two points 
        on the earth (specified in decimal degrees)
        """
        # convert decimal degrees to radians 
        lon1, lat1, lon2, lat2 = map(radians, [lon1, lat1, lon2, lat2])
    
        # haversine formula 
        dlon = lon2 - lon1 
        dlat = lat2 - lat1 
        a = sin(dlat/2)**2 + cos(lat1) * cos(lat2) * sin(dlon/2)**2
        c = 2 * asin(sqrt(a)) 
        r = 3956 
        return c * r
    

    calculate each properties distance to washington monument and capital one arena

    In [21]:
    ls_d["distance_to_monument"] = ls_d[['latitude', 'longitude']] \
        .apply(lambda row: haversine(row['latitude'], row['longitude'], monument.get("lat"), monument.get("lon")), axis=1)
    
    
    ls_d["distance_to_arena"] = ls_d[['latitude', 'longitude']] \
        .apply(lambda row: haversine(row['latitude'], row['longitude'], arena.get("lat"), arena.get("lon")), axis=1)
    
    
    ls_d["distance_to_center"] = ls_d[['latitude', 'longitude']] \
        .apply(lambda row: haversine(row['latitude'], row['longitude'], center.get("lat"), center.get("lon")), axis=1)
    
    
    ls_d["distance_to_whitehouse"] = ls_d[['latitude', 'longitude']] \
        .apply(lambda row: haversine(row['latitude'], row['longitude'], wh.get("lat"), wh.get("lon")), axis=1)
    
    In [22]:
    distance_corrs = pd.concat([ls_d.select_dtypes(include=np.number), pd.get_dummies(ls_d['Price Decile'])], axis=1).corr()[["distance_to_monument", "distance_to_arena", "distance_to_center", "distance_to_whitehouse"]].reset_index().dropna()
    
    In [23]:
    px.scatter(ls_d[ls_d.review_scores_location.notnull()], x="distance_to_monument", y="review_scores_location", color="review_scores_location", trendline='lowess')
    
    In [24]:
    distance_corrs.round(3).sort_values("distance_to_whitehouse").style.background_gradient(low=0.0, high=.10)
    
    Out[24]:
    index distance_to_monument distance_to_arena distance_to_center distance_to_whitehouse
    9 latitude -0.375000 -0.158000 -0.373000 -0.354000
    37 review_scores_location -0.280000 -0.137000 -0.256000 -0.255000
    40 calculated_host_listings_count_entire_homes -0.163000 -0.088000 -0.150000 -0.153000
    39 calculated_host_listings_count -0.156000 -0.087000 -0.144000 -0.147000
    6 host_listings_count -0.159000 -0.074000 -0.139000 -0.143000
    7 host_total_listings_count -0.159000 -0.074000 -0.139000 -0.143000
    15 square_feet -0.133000 -0.103000 -0.139000 -0.141000
    24 minimum_nights_avg_ntm -0.115000 -0.041000 -0.096000 -0.098000
    21 maximum_minimum_nights -0.115000 -0.042000 -0.096000 -0.098000
    54 (135.0, 165.0] -0.074000 -0.057000 -0.073000 -0.074000
    16 price -0.060000 -0.038000 -0.054000 -0.056000
    57 (350.0, 10000.0] -0.064000 -0.040000 -0.052000 -0.055000
    20 minimum_minimum_nights -0.062000 -0.017000 -0.053000 -0.054000
    55 (165.0, 215.0] -0.052000 -0.041000 -0.051000 -0.053000
    56 (215.0, 350.0] -0.036000 -0.043000 -0.036000 -0.038000
    53 (115.0, 135.0] -0.037000 -0.020000 -0.033000 -0.033000
    52 (99.0, 115.0] -0.034000 -0.023000 -0.032000 -0.032000
    18 minimum_nights -0.042000 0.002000 -0.032000 -0.031000
    51 (85.0, 99.0] -0.015000 0.016000 -0.012000 -0.009000
    33 review_scores_accuracy 0.004000 -0.011000 -0.002000 -0.002000
    1 scrape_id 0.000000 -0.000000 0.000000 -0.000000
    19 maximum_nights 0.005000 -0.003000 0.004000 0.003000
    22 minimum_maximum_nights 0.005000 -0.003000 0.004000 0.003000
    23 maximum_maximum_nights 0.005000 -0.003000 0.004000 0.003000
    25 maximum_nights_avg_ntm 0.005000 -0.003000 0.004000 0.003000
    32 review_scores_rating 0.007000 -0.002000 0.005000 0.005000
    30 number_of_reviews 0.026000 -0.015000 0.021000 0.020000
    29 availability_365 0.021000 0.005000 0.023000 0.022000
    41 calculated_host_listings_count_private_rooms 0.028000 0.017000 0.026000 0.026000
    12 bathrooms 0.037000 -0.003000 0.030000 0.029000
    34 review_scores_cleanliness 0.034000 0.016000 0.031000 0.031000
    42 calculated_host_listings_count_shared_rooms 0.052000 -0.025000 0.036000 0.034000
    50 (70.0, 85.0] 0.045000 0.023000 0.037000 0.039000
    36 review_scores_communication 0.045000 0.026000 0.041000 0.042000
    38 review_scores_value 0.048000 0.019000 0.041000 0.042000
    31 number_of_reviews_ltm 0.054000 0.001000 0.047000 0.046000
    17 guests_included 0.063000 -0.017000 0.049000 0.047000
    35 review_scores_checkin 0.050000 0.044000 0.048000 0.050000
    0 id 0.065000 0.019000 0.059000 0.056000
    43 reviews_per_month 0.069000 0.002000 0.060000 0.058000
    11 accommodates 0.081000 -0.011000 0.067000 0.064000
    14 beds 0.087000 0.019000 0.079000 0.077000
    5 host_id 0.093000 0.032000 0.083000 0.082000
    49 (53.0, 70.0] 0.088000 0.066000 0.083000 0.085000
    27 availability_60 0.088000 0.047000 0.086000 0.085000
    28 availability_90 0.091000 0.043000 0.087000 0.086000
    13 bedrooms 0.100000 0.022000 0.088000 0.087000
    26 availability_30 0.102000 0.052000 0.098000 0.098000
    48 (-0.001, 53.0] 0.175000 0.118000 0.165000 0.167000
    10 longitude 0.847000 0.231000 0.717000 0.708000
    45 distance_to_arena 0.625000 1.000000 0.779000 0.793000
    44 distance_to_monument 1.000000 0.625000 0.967000 0.964000
    46 distance_to_center 0.967000 0.779000 1.000000 0.999000
    47 distance_to_whitehouse 0.964000 0.793000 0.999000 1.000000

    Answer: slightly negative assocaition for wh/monumnet distances, location review scores, host listing count. Weak postive correlation betweeen bedrooms, availability and lowest-priced decile of listings.

    Question 2: What kind of properties are these listings (property type, room type, bedroom count, bathroom count, etc.)

    Property Types: 44% Apartments, 20% Houses, 15% Townhouses (3 form biggest share)

    In [25]:
    proptypes = (
                    ls_d.
                        groupby("property_type").count()["price"]
                        .reset_index().rename({'price':'# of listings'}, axis=1)
    )
    
    
    (
        proptypes
            .sort_values("# of listings", ascending=False)
            .assign(share=proptypes["# of listings"]/ls_d.shape[0])
            .style.background_gradient(cmap='Purples', subset=['share'])
        
    
    )
    
    Out[25]:
    property_type # of listings share
    1 Apartment 4127 0.442384
    15 House 1933 0.207203
    21 Townhouse 1402 0.150284
    8 Condominium 764 0.081895
    11 Guest suite 547 0.058634
    19 Serviced apartment 241 0.025833
    16 Loft 69 0.007396
    12 Guesthouse 53 0.005681
    3 Bed and breakfast 47 0.005038
    5 Boutique hotel 41 0.004395
    13 Hostel 34 0.003645
    17 Other 20 0.002144
    6 Bungalow 17 0.001822
    14 Hotel 9 0.000965
    22 Villa 8 0.000858
    4 Boat 4 0.000429
    20 Tiny house 4 0.000429
    0 Aparthotel 3 0.000322
    2 Barn 2 0.000214
    10 Dome house 1 0.000107
    9 Cottage 1 0.000107
    7 Camper/RV 1 0.000107
    18 Resort 1 0.000107
    In [55]:
    prop_fig = px.bar(ls_d.groupby(["neighbourhood", "property_type"])['id'].count().reset_index().rename({"id":"listing count"},axis=1).sort_values("listing count",ascending=False),
           title="Property Type by Neighborhood",
           x='neighbourhood', y="listing count", color="property_type", template="plotly_dark", height=900, color_discrete_sequence=plotly.colors.qualitative.Light24)
    
    In [56]:
    prop_fig.write_image("property_type.png",height=900, width=1500)
    

    Room Types: We can see that the overwhelming majority of listings are entire homes/apartments ( 71%) , followed by private rooms (~25%).

    In [157]:
    # set token
    px.set_mapbox_access_token("pk.eyJ1IjoibGF3cmVuY2VkIiwiYSI6ImNrODFzZnFnNzA0YmczZW9nNWN4aTFvdngifQ.VlB5-L7owXKEXo8JEePk7w")
    fig = px.scatter_mapbox(ls_d.dropna(subset=['beds']), lat="latitude", lon="longitude", color="room_type", template="simple_white", size="beds",
                              hover_data=["neighbourhood", "price"],
                               center={"lat": 38.895, "lon": -77.024},
                               mapbox_style="basic", zoom=12)
    
    fig.update_layout(margin={"r":5,"t":0,"l":0,"b":0})
    
    fig.write_image("room_type.png", width=1500, height=900)
    
    In [57]:
    roomtypes = (
                    ls_d.
                        groupby("room_type").count()["price"]
                        .reset_index().rename({'price':'# of listings'}, axis=1)
    )
    
    
    (
        roomtypes
            .sort_values("# of listings", ascending=False)
            .assign(share=roomtypes["# of listings"]/ls_d.shape[0])
            .style.background_gradient(cmap='Blues', subset=['share'])
        
    
    )
    
    Out[57]:
    room_type # of listings share
    0 Entire home/apt 6652 0.713045
    2 Private room 2414 0.258763
    3 Shared room 219 0.023475
    1 Hotel room 44 0.004716

    Summary statistics for type of property types: mode of price, bathrooms, bedrooms and beds

    In [58]:
    prop_df = ls_d[['price', 'bathrooms','bedrooms','beds', 'property_type']].copy()
    
    (
        prop_df
            .groupby("property_type").agg(lambda x:x.value_counts().index[0]).reset_index()
            .sort_values("bedrooms",ascending=False)
            .style.background_gradient(cmap='Oranges', subset=["bathrooms","bedrooms", "beds"])
    )
    
    Out[58]:
    property_type price bathrooms bedrooms beds
    10 Dome house 300.000000 4.500000 4.000000 4.000000
    2 Barn 275.000000 3.000000 3.000000 3.000000
    4 Boat 99.000000 1.000000 2.000000 5.000000
    0 Aparthotel 253.000000 1.000000 1.000000 1.000000
    13 Hostel 7000.000000 1.000000 1.000000 6.000000
    21 Townhouse 150.000000 1.000000 1.000000 1.000000
    19 Serviced apartment 369.000000 1.000000 1.000000 1.000000
    18 Resort 500.000000 1.000000 1.000000 2.000000
    17 Other 272.000000 1.000000 1.000000 1.000000
    16 Loft 400.000000 1.000000 1.000000 2.000000
    15 House 75.000000 1.000000 1.000000 1.000000
    14 Hotel 40.000000 1.000000 1.000000 2.000000
    11 Guest suite 100.000000 1.000000 1.000000 1.000000
    12 Guesthouse 120.000000 1.000000 1.000000 1.000000
    1 Apartment 100.000000 1.000000 1.000000 1.000000
    9 Cottage 100.000000 1.000000 1.000000 1.000000
    8 Condominium 150.000000 1.000000 1.000000 1.000000
    6 Bungalow 100.000000 1.000000 1.000000 1.000000
    5 Boutique hotel 100.000000 1.000000 1.000000 1.000000
    3 Bed and breakfast 189.000000 1.000000 1.000000 1.000000
    22 Villa 80.000000 1.000000 1.000000 1.000000
    7 Camper/RV 50.000000 1.000000 0.000000 5.000000
    20 Tiny house 175.000000 1.000000 0.000000 1.000000

    Question 3 (Host Analysis): Are there hosts that have multiple listings/have businesses using Aribnb? Do these hosts have good scores and reviews? Do they focus in one or two neighbourhoods? Focus on ceratin price points?

    Are there hosts that have multiple listings/have businesses using Aribnb?

    In [59]:
    ls_subset = ls_d[['id', 'host_id','host_name', 'host_about', 'host_response_time','host_since', 
          'host_response_rate', 'host_acceptance_rate', 'host_is_superhost', 'host_listings_count', 'host_total_listings_count', 'neighbourhood',
          'latitude', 'longitude', 'room_type', 'property_type', 'bathrooms', 'bedrooms', 'beds', 'price', 'minimum_nights', 'maximum_nights',
          'availability_30', 'availability_60', 'availability_90','availability_365', 'number_of_reviews', 'number_of_reviews_ltm', 
          'review_scores_rating','review_scores_accuracy','review_scores_cleanliness','review_scores_checkin','review_scores_communication','review_scores_location','review_scores_value',
          'reviews_per_month']].copy()
    
    In [60]:
    # hosts that have more than one listing 
    host_summary = (
        ls_subset
            .groupby(["host_id", "host_name"])
            .count().reset_index()
            .sort_values("id", ascending=False)
            .iloc[:,:3]
            .rename({"id":"# of listings"}, axis=1)
        )
    
    host_summary_multiple = host_summary[host_summary["# of listings"] > 1]
    

    Answer 1: 46% of the listings are owned by hosts with multiple listings. Thes guys are probably running businesses

    In [61]:
    np.sum(host_summary_multiple["# of listings"]) / np.sum(host_summary["# of listings"]) 
    
    Out[61]:
    0.4590480274442539

    Do these hosts have good scores and reviews?

    In [62]:
    host_reviews = host_summary_multiple.merge(ls_subset, on = ['host_id', 'host_name'])
    
    In [63]:
    # weight the scores of the hosts that have more than one listing by "reviews per month"
    host_grouped = (
                    host_reviews
                        .dropna()
                        .groupby(['host_id', 'host_name'])
                        [['review_scores_rating','review_scores_accuracy','review_scores_cleanliness','review_scores_checkin','review_scores_communication','review_scores_location','review_scores_value',
                              'reviews_per_month']]
    )
    
    (
        # collect weighted averages 
        host_grouped
            .apply(lambda x: pd.Series(np.average(x[['review_scores_rating','review_scores_accuracy',
                                                     'review_scores_cleanliness','review_scores_checkin','review_scores_communication',
                                                     'review_scores_location','review_scores_value']], weights=x["reviews_per_month"], axis=0),
                                       ['review_scores_rating','review_scores_accuracy','review_scores_cleanliness','review_scores_checkin',
                                        'review_scores_communication','review_scores_location','review_scores_value']))
        
        # merge in helpful information 
        .merge(host_summary_multiple, on=['host_id', 'host_name'])
        .sort_values("# of listings", ascending=False)
        .style.background_gradient(cmap='RdYlGn',subset=['review_scores_rating','review_scores_accuracy','review_scores_cleanliness','review_scores_checkin',
                                        'review_scores_communication','review_scores_location','review_scores_value'])
        
    )
    
    Out[63]:
    host_id host_name review_scores_rating review_scores_accuracy review_scores_cleanliness review_scores_checkin review_scores_communication review_scores_location review_scores_value # of listings
    425 48005494 Zeus 97.086214 9.805689 9.760613 9.821444 9.677899 9.865208 9.032823 186
    521 107434423 Blueground 95.125448 9.784946 9.412186 8.716846 9.419355 9.261649 8.172043 165
    420 46630199 Home Sweet City 96.511743 9.911369 9.966854 9.951760 9.960031 9.873694 9.523818 64
    398 39930655 Nicole & 95.904617 9.780509 9.777721 9.820006 9.926161 9.986314 9.642124 62
    11 17633 Charlotte (& Anna-Regina) 94.839660 9.494901 9.703966 9.705382 9.411048 9.900000 9.424363 54
    195 8008783 Cliff 92.767093 9.592485 9.399380 9.766705 9.016076 9.922913 9.180322 51
    293 19131071 Agnes 60.000000 2.000000 4.000000 10.000000 10.000000 10.000000 8.000000 29
    36 487806 Stay BnB DC 92.772523 9.366132 9.343984 9.910601 9.826047 9.934710 9.112239 27
    470 69203193 Leyla And Matt 92.241787 9.305314 9.308213 9.752899 9.689372 9.863768 9.111111 27
    30 315148 John 92.733882 9.153635 8.315501 9.687243 9.543210 9.382716 9.117970 25
    274 16644446 Corporate 94.028037 9.651090 9.383178 9.666667 9.780374 9.954829 9.401869 24
    219 10292686 Brian 97.361546 9.967801 9.935603 10.000000 10.000000 10.000000 9.728611 23
    145 4962900 Stay Alfred 91.311659 9.587444 9.313901 8.961883 9.199552 9.802691 9.260090 23
    499 95459395 Bluebird 89.000000 9.586777 9.586777 9.380165 8.818182 9.793388 8.446281 22
    529 122382567 Evolve 91.048193 9.693976 9.024096 10.000000 10.000000 9.903614 9.296386 20
    157 5487930 Mark 93.680451 9.501880 8.832707 9.659774 9.849624 10.000000 9.214286 19
    384 35549750 Morgan 95.729515 9.823389 9.125961 9.982233 10.000000 9.919915 9.848846 19
    247 13817352 Pam 93.914622 9.565260 9.847890 9.942427 9.976120 9.981354 9.038927 17
    594 219493221 Sonder (D.C.) 95.832407 9.851852 9.783333 9.929167 9.944444 9.214815 9.459259 17
    567 177578477 Jarrick, Ferris, Rita 93.862314 9.700497 9.367637 10.000000 9.771469 9.239177 9.403123 16
    120 3672670 Dennis 88.844437 9.080344 8.537940 9.988924 9.745412 8.934865 8.861299 16
    353 26180779 Will 91.576214 9.456951 8.960134 9.926633 9.960134 9.879732 9.089112 16
    76 1864688 Blü Properties 96.710602 9.932665 9.561605 10.000000 10.000000 10.000000 9.524037 15
    316 21493494 RJ Management LLC 96.044187 9.959075 9.645611 10.000000 10.000000 9.914887 9.654211 15
    613 277396425 Convention Center Exclusive Flats 87.305365 8.840440 9.685007 7.903714 9.832187 9.059147 9.242091 15
    85 2131753 Shannon 93.876289 9.807560 9.285223 9.470790 9.704467 10.000000 9.278351 12
    209 9419684 Mike 90.865052 8.141869 9.837370 8.269896 8.346021 10.000000 9.730104 12
    27 138459 L 99.720000 10.000000 10.000000 10.000000 9.773333 10.000000 9.946667 12
    337 23714240 Nana 92.221102 9.617953 9.612913 9.937008 9.748031 9.547717 9.120945 11
    419 46582948 Huxley 97.398793 9.934449 9.889194 9.895228 10.000000 9.909490 9.624520 11
    554 158057505 Namastay 92.127530 9.440536 9.382338 9.382338 9.464828 9.614119 9.000000 11
    595 219971307 Abrahim 94.414663 9.788462 9.348558 9.836538 9.902644 9.539663 9.555288 11
    254 14692970 P 91.480916 9.366412 9.811069 9.009542 9.303435 9.835878 9.000000 10
    113 3448315 Allen 94.588939 9.704036 9.562033 9.977578 9.970105 9.696562 9.623318 10
    607 250464525 Morgan 88.883529 9.284706 8.822353 9.075294 9.652941 9.327059 9.652941 10
    438 54126082 Jason 93.904282 9.908480 9.673384 9.733837 9.487825 9.753988 9.422334 10
    251 14275599 Heather 98.277384 10.000000 10.000000 10.000000 10.000000 10.000000 10.000000 9
    503 98799358 Jacob 97.469449 10.000000 10.000000 10.000000 10.000000 9.233979 9.628912 9
    615 288033145 Linas 92.375000 10.000000 9.750000 9.500000 9.125000 9.750000 9.750000 9
    586 205105773 Anita 96.733333 9.764198 9.730864 10.000000 10.000000 10.000000 9.764198 9
    492 90860645 Phillip 95.452944 10.000000 9.560644 10.000000 9.638148 9.296930 9.754404 9
    564 174792040 RoomPicks By Victoria 86.204301 9.643011 9.569892 9.903226 10.000000 8.944086 8.847312 9
    125 3847743 Ronald 89.471724 9.242069 9.050345 9.308966 9.353793 9.931724 8.982069 9
    360 28011820 Marcy 93.757301 9.901309 9.810675 9.852971 9.877140 10.000000 9.117825 9
    246 13743641 Eric 94.673727 9.835603 9.792738 10.000000 10.000000 9.683308 9.835603 9
    590 208747065 Abdo 90.878918 9.597419 9.308543 9.499078 9.326982 9.849416 9.064536 8
    294 19143862 Louise 89.388158 9.197368 8.914474 9.493421 9.289474 9.756579 8.460526 8
    286 18048636 Andrew 89.487500 9.075000 8.892500 9.627500 9.897500 9.957500 8.802500 8
    410 44949993 Mike 92.293965 9.532122 9.243997 10.000000 10.000000 9.327709 9.195977 8
    64 1443163 Dian 93.611058 9.907531 9.113441 10.000000 10.000000 9.907531 9.245949 8
    612 269834634 The Residence Of Kalorama 96.981213 10.000000 10.000000 10.000000 10.000000 10.000000 9.785369 8
    216 9794313 Steven And Michelle 95.355646 9.828820 9.510475 10.000000 10.000000 9.677312 9.381962 8
    363 28827689 Carolyn 92.301500 9.597000 9.360500 10.000000 9.641000 10.000000 9.109000 8
    117 3574033 Grace 88.998208 9.041219 9.118280 9.118280 9.021505 9.573477 9.021505 8
    162 5778691 Corbett 97.116894 10.000000 10.000000 10.000000 10.000000 10.000000 9.547638 7
    229 11496710 Emily 91.380590 9.182683 9.137012 10.000000 9.664764 8.182683 8.645734 7
    359 27323824 Karl 95.452748 9.524129 9.851542 10.000000 10.000000 9.795576 9.524129 7
    299 19979392 Christopher 99.109244 10.000000 10.000000 10.000000 10.000000 9.315126 10.000000 7
    405 44203219 Hayk 96.725125 10.000000 9.437243 10.000000 10.000000 10.000000 9.527856 7
    409 44706521 David 93.564062 10.000000 9.570312 9.650000 9.784375 9.698438 9.735937 7
    372 31488625 Steve 97.677235 10.000000 10.000000 10.000000 10.000000 9.567533 9.753963 7
    401 41047431 Lisa 95.821718 9.802269 9.369530 10.000000 10.000000 10.000000 9.430578 7
    546 140334352 Beko 96.085714 10.000000 9.533333 10.000000 10.000000 9.261905 9.261905 6
    239 12166019 Yousong & Sharon 89.812680 9.423631 8.481268 9.786744 9.622478 9.000000 8.893372 6
    235 11980555 Lea 94.142584 9.875557 9.658498 10.000000 10.000000 9.875557 9.160726 6
    52 1131910 Adam 81.996552 8.527586 8.127586 9.217241 9.544828 9.217241 8.889655 6
    600 234630912 Kamran 96.266769 10.000000 9.828615 10.000000 10.000000 9.000000 9.795205 6
    38 511136 Tommy 94.052105 9.764737 9.794211 9.985263 9.992632 10.000000 8.981053 6
    436 53580080 Sojourn 96.914989 10.000000 10.000000 10.000000 10.000000 10.000000 9.586130 6
    511 102274654 Rae 94.387908 9.863688 9.548254 10.000000 9.863688 9.195644 9.366879 6
    362 28825130 Greg 97.948085 10.000000 10.000000 10.000000 10.000000 10.000000 9.487482 6
    243 13239338 Sleep Sweets DC Project 97.229017 9.862110 10.000000 10.000000 10.000000 10.000000 9.862110 6
    533 124782850 Jenna 91.590164 9.627049 9.688525 9.905738 9.622951 9.500000 9.405738 6
    611 264990197 Griffen 99.190123 10.000000 9.841975 10.000000 10.000000 9.930864 9.930864 6
    462 65548398 Darryl 97.360920 9.903448 9.754023 10.000000 9.903448 9.903448 9.627586 6
    506 101353697 Cathy 97.635135 10.000000 9.527027 9.527027 10.000000 10.000000 9.527027 6
    149 5096803 Akosua 93.418936 9.961089 9.552529 9.748379 10.000000 8.911803 9.640726 6
    434 52703422 Erin 96.708824 9.711765 9.508824 10.000000 10.000000 9.618382 9.614706 6
    487 86884249 Dr. J 84.365107 8.745081 7.538260 9.000000 9.000000 9.000000 8.586358 6
    620 315862664 Tashi 97.481308 10.000000 9.328660 10.000000 10.000000 8.968847 9.640187 6
    584 198612938 Sarah 96.694837 10.000000 10.000000 9.793847 10.000000 9.826379 9.410184 6
    161 5706129 Aric 86.738241 9.088957 8.672802 9.626789 9.626789 8.785276 8.912065 6
    566 177085783 Firtuna & Mike 89.742049 9.204947 9.000000 10.000000 9.577739 9.000000 8.934629 6
    526 110052340 Josephine 85.330472 8.000000 9.776824 8.000000 8.888412 8.888412 8.888412 6
    614 285718253 Nick 91.127639 9.829175 9.029750 9.413628 9.831094 10.000000 8.587332 6
    244 13275854 John 97.804213 10.000000 10.000000 10.000000 10.000000 10.000000 10.000000 5
    187 7042945 RoDeece 95.473019 10.000000 9.000000 10.000000 10.000000 9.501686 9.249578 5
    153 5243080 Michael 95.483034 9.884232 9.884232 9.379242 9.884232 10.000000 9.379242 5
    191 7636690 Rover 96.672538 10.000000 9.614892 10.000000 10.000000 9.614892 9.336269 5
    618 304729379 Holden 90.557596 9.332220 9.549249 10.000000 9.332220 9.332220 9.021703 5
    215 9680354 Adam 97.908160 10.000000 10.000000 10.000000 10.000000 9.543189 9.543189 5
    207 9354986 Jonathon 96.103896 10.000000 9.220779 10.000000 10.000000 9.220779 10.000000 5
    203 9263895 Payam 96.191121 10.000000 9.482318 10.000000 10.000000 10.000000 9.370203 5
    616 293128885 Lia 100.000000 10.000000 9.166667 10.000000 10.000000 10.000000 10.000000 5
    204 9286163 Olena 92.153846 8.895105 8.722028 9.895105 10.000000 10.000000 9.000000 5
    535 126333441 Thang 93.486653 9.437372 9.258316 10.000000 10.000000 9.331417 8.793429 5
    277 16897313 Charlie & Christian 97.210588 9.809412 9.437647 10.000000 10.000000 9.540000 9.809412 5
    580 189066137 Charles 81.202808 8.444618 8.149766 9.151326 9.141966 6.536661 8.191888 5
    542 137359094 John 97.792760 9.947511 10.000000 10.000000 10.000000 9.957466 9.957466 5
    523 107842986 Ina 97.922234 10.000000 10.000000 10.000000 10.000000 9.194962 10.000000 5
    519 106426179 Deb 100.000000 10.000000 10.000000 10.000000 10.000000 10.000000 10.000000 5
    509 102067019 Hope 77.444444 7.333333 6.666667 8.055556 8.055556 7.500000 6.777778 5
    491 90117890 Jonathan 97.261616 10.000000 9.772727 10.000000 10.000000 10.000000 9.772727 5
    486 86401186 Paul 98.134139 10.000000 10.000000 10.000000 10.000000 10.000000 10.000000 5
    480 78194147 Genet 97.636364 9.312564 10.000000 9.636364 9.636364 9.636364 9.636364 5
    456 63907155 Tsholofelo 88.333333 7.666667 10.000000 9.791667 10.000000 8.625000 9.791667 5
    450 62543911 Monica 69.996933 6.325153 7.552147 8.776074 8.776074 7.549080 6.325153 5
    444 58166745 Mark 97.062372 10.000000 9.985685 10.000000 10.000000 10.000000 9.542434 5
    417 46521595 Kerry Ann And Steve 97.628472 10.000000 9.791934 10.000000 9.619124 10.000000 9.597489 5
    610 260135411 Marvin 98.256410 10.000000 10.000000 10.000000 9.688034 9.380342 10.000000 5
    416 46194019 Carmelita 76.634286 7.948571 7.162286 9.160000 9.109714 9.800000 8.160000 5
    412 45390384 Ari 94.193651 10.000000 10.000000 9.533333 9.753968 10.000000 9.238095 5
    402 41648044 Azi & Arash 97.395095 9.915531 9.915531 10.000000 10.000000 10.000000 9.177112 5
    399 40506199 Tom 98.057143 8.942857 10.000000 10.000000 9.428571 10.000000 10.000000 5
    390 37782082 Anne Marie 96.042462 10.000000 9.633846 9.849231 10.000000 9.811692 9.671385 5
    589 208577817 Tiffany And Marquell 97.566999 10.000000 10.000000 10.000000 10.000000 10.000000 10.000000 5
    328 22801245 Selina And Veronica 95.558528 9.593645 9.423077 9.934783 9.804348 8.612040 8.886288 5
    321 22198100 Bez 99.254054 10.000000 10.000000 10.000000 10.000000 9.605405 9.978378 5
    602 241121781 Donnell 94.190596 9.079624 9.778683 9.072100 9.778683 9.778683 9.293417 5
    295 19214127 David 94.904032 10.000000 9.429839 10.000000 10.000000 9.768548 10.000000 5
    0 2798 Ayeh 93.990859 9.361974 9.140768 10.000000 10.000000 9.903108 9.043876 5
    623 324906850 Generator 27.000000 2.000000 6.000000 5.000000 2.000000 9.000000 3.000000 5
    111 3331633 Joe 93.365854 9.564024 9.051829 9.926829 9.926829 8.524390 9.051829 5
    19 55009 Mala 96.568182 9.840909 9.568182 10.000000 10.000000 9.431818 9.568182 5
    114 3474736 Sam 97.139303 10.000000 10.000000 10.000000 10.000000 10.000000 9.683250 5
    42 738459 Natasha 94.101852 9.194444 9.000000 10.000000 10.000000 9.194444 9.000000 5
    202 9011212 David 99.253521 10.000000 10.000000 10.000000 10.000000 10.000000 10.000000 4
    155 5448602 Maureen 98.213262 10.000000 10.000000 10.000000 10.000000 10.000000 10.000000 4
    507 101670166 Elgun 90.444776 9.712239 8.296119 10.000000 10.000000 10.000000 9.008358 4
    115 3517743 Kanita 89.314685 9.020979 9.020979 10.000000 9.776224 8.370629 8.797203 4
    110 3324376 Alex 93.783036 10.000000 9.751786 9.000000 10.000000 9.706250 9.000000 4
    24 95464 Syga 86.676333 8.423761 8.194107 9.000000 8.194107 8.416277 8.711880 4
    160 5571655 Aimee 100.000000 10.000000 10.000000 10.000000 10.000000 10.000000 9.653061 4
    598 231272898 Hostel Comfort 91.742021 9.462766 9.000000 10.000000 9.251773 9.000000 9.061170 4
    74 1662757 Gloria 97.261224 10.000000 10.000000 10.000000 10.000000 10.000000 9.110204 4
    188 7196325 Jessica 94.578947 9.045372 9.045372 10.000000 10.000000 10.000000 9.045372 4
    15 32015 Teresa 92.501792 9.182796 9.000000 10.000000 10.000000 10.000000 9.302867 4
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    331 23083652 Robert 89.000000 9.000000 9.000000 9.000000 10.000000 9.000000 9.000000 2
    330 22932609 Martin 98.000000 10.000000 10.000000 10.000000 10.000000 9.811236 10.000000 2
    329 22801633 Natascha 97.404995 10.000000 10.000000 10.000000 10.000000 10.000000 9.404995 2
    82 2070536 Joel 99.000000 10.000000 10.000000 10.000000 10.000000 10.000000 10.000000 2
    326 22717482 Lynette 97.948905 10.000000 10.000000 10.000000 10.000000 10.000000 10.000000 2
    325 22490527 Alison 99.000000 10.000000 10.000000 10.000000 10.000000 10.000000 10.000000 2
    324 22456488 Synta 98.642599 10.000000 10.000000 10.000000 10.000000 9.642599 10.000000 2
    323 22289377 Douglas 99.000000 10.000000 10.000000 10.000000 10.000000 10.000000 10.000000 2
    322 22239362 Linden 97.509722 10.000000 10.000000 10.000000 10.000000 10.000000 10.000000 2
    83 2077502 Ross 94.000000 10.000000 9.000000 10.000000 10.000000 10.000000 10.000000 2
    320 22099627 Craig 73.000000 7.000000 7.000000 7.000000 7.000000 7.000000 7.000000 2
    84 2127232 Wally 70.870748 8.639456 7.278912 10.000000 10.000000 9.680272 8.319728 2
    314 21406323 Anna 98.527615 10.000000 10.000000 10.000000 10.000000 10.000000 10.000000 2
    311 20917831 Irik 97.553159 10.000000 10.000000 10.000000 10.000000 10.000000 10.000000 2
    310 20916687 Derric 90.263889 9.000000 9.000000 9.631944 10.000000 9.368056 9.000000 2
    309 20915410 Rachael 97.987654 10.000000 10.000000 10.000000 10.000000 10.000000 10.000000 2
    308 20870905 Yll 97.000000 10.000000 9.754098 10.000000 10.000000 10.000000 10.000000 2
    307 20796158 B 96.000000 9.000000 10.000000 10.000000 10.000000 9.000000 9.000000 2
    305 20495214 Marcelo 90.925725 9.000000 8.000000 8.000000 7.000000 10.000000 9.000000 2
    312 20970565 Sondra 96.186441 10.000000 10.000000 10.000000 10.000000 9.186441 10.000000 2

    Answer 2: We do not see see too much fluctuation in the scores here, except for a couple bad apples with low accuracy scores as well. It generally looks like the review scores are generally postive for the top hosts and overall

    In [64]:
    ls_d[[col for col in ls_d.columns if "review" in col]].mean()
    
    Out[64]:
    number_of_reviews              40.574445
    number_of_reviews_ltm          14.428127
    review_scores_rating           95.106292
    review_scores_accuracy          9.727160
    review_scores_cleanliness       9.546770
    review_scores_checkin           9.837447
    review_scores_communication     9.817192
    review_scores_location          9.672226
    review_scores_value             9.540403
    reviews_per_month               1.895331
    dtype: float64
    In [117]:
    rs_d.head()
    
    Out[117]:
    listing_id id date reviewer_id reviewer_name comments
    0 3362 662 2009-01-21 6917 Robyn Ayeh is a gracious and accommodating host. He...
    1 3362 1099 2009-03-26 10492 Brian I stayed at Ayeh's place in March, and it was ...
    2 3362 6300 2009-07-28 22668 Anthony My 14 y.o. niece, her best friend and I stayed...
    3 3362 8387 2009-08-27 30379 Lina We enjoyed the time with Ayeh in Washington ve...
    4 3362 8451 2009-08-28 33059 Nicole We also enjoyed our stay in DC while at Ayeh's...

    Do they focus in one or two neighbourhoods? Focus on ceratin price points?

    In [65]:
    # top 100 hosts by # of listings 
    top100hosts = host_summary[:100].host_id.values.tolist()
    
    # get mean price of listings acorss neighbourhoods for each host with multiple reviews
    prices_neighbourhoods = \
    (
        host_reviews[['id', 'host_name', 'host_id', 'price','neighbourhood']]
        .groupby(["host_name","host_id"])
        .agg({'price':'mean', 'neighbourhood':lambda x:x.value_counts().index[0]}, axis=1)
        .reset_index()
        .rename({'price':'mean_price'}, axis=1)
    )
    
    multiples = prices_neighbourhoods.merge(host_summary[['host_id','# of listings']], on='host_id').sort_values("# of listings", ascending=False)
    
    In [66]:
    host_summary_multiple.head()
    
    Out[66]:
    host_id host_name # of listings
    3649 48005494 Zeus 186
    4853 107434423 Blueground 165
    3606 46630199 Home Sweet City 64
    3346 39930655 Nicole & 62
    31 17633 Charlotte (& Anna-Regina) 54

    Asnwer 3 Part 1: It looks like out of the top 1078 hosts with multiple listings, 825 of them (~77%) are in only one neighourhood

    In [67]:
    h_summary = \
    (
        ls[ls.host_id.isin(host_summary_multiple.host_id.unique())]
       .groupby(["host_id", "neighbourhood"])["id"].agg(["count", "sum"]).reset_index()
    )
    
    In [68]:
    len(h_summary.host_id.value_counts().reset_index().query('host_id > 1')) / len(h_summary.host_id.unique())
    
    Out[68]:
    0.23469387755102042
    In [69]:
    multi_neigh = h_summary.host_id.value_counts().reset_index().query('host_id > 1')['index'].unique().tolist()
    
    In [70]:
    sns.catplot(height=10, kind='bar',aspect=1.5,
        data = h_summary.groupby("neighbourhood")['count'].sum().reset_index().sort_values("count", ascending=False)
                .rename({'count':'Number of listings', 'neighbourhood':'Neighborhood' }, axis=1),
        y='Neighborhood', x='Number of listings')
    
    Out[70]:
    <seaborn.axisgrid.FacetGrid at 0x1a2ac34780>

    Asnwer 3 Part 2: With the average IQR (of the middle 80%) of hosts with multiple listings being $40, it looks like these "businesses" tend to foucs on certain price points. There is no strong linear relationship between number of listings and IQR eitherm with the correlation around 8%

    In [71]:
    q3 = ls[ls.host_id.isin(multiples.host_id.unique())].groupby(["host_id"])["price"].describe().reset_index()
    
    # filter out outliers
    q3_filtered = q3[q3["mean"].between(q3["mean"].quantile(.05), q3["mean"].quantile(.95))]
    
    q3_filtered["IQR"] = q3_filtered["75%"] - q3_filtered["25%"]
                                                                                                                                                          
    q3_filtered.sort_values("count", ascending=False).style.background_gradient(cmap="RdYlGn", subset=["25%", "50%", "75%", "IQR"])
    
    /Users/ldugom/.local/lib/python3.6/site-packages/ipykernel_launcher.py:6: SettingWithCopyWarning:
    
    
    A value is trying to be set on a copy of a slice from a DataFrame.
    Try using .loc[row_indexer,col_indexer] = value instead
    
    See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
    
    
    Out[71]:
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    639 48005494 186.000000 160.505376 46.191639 60.000000 128.250000 149.000000 179.000000 380.000000 50.750000
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    In [72]:
    # what is the mean 
    q3_filtered["IQR"].mean()
    
    Out[72]:
    43.249743326488705
    In [82]:
    # does IQR tend to go up with # of listings?
    q3_filtered["mean"].corr(q3_filtered["IQR"])
    
    Out[82]:
    0.722507708254381
    In [132]:
    iqr = px.scatter(q3_filtered.rename({"mean":"Mean Price of listings", "50%":"50th percentile"},axis=1), x="Mean Price of listings", y='IQR', color='50th percentile', trendline='ols', 
                     template="ggplot2", color_continuous_scale=plotly.colors.sequential.Darkmint,
              title="<b>Mean Price vs IQR </b> (hosts with multiple listings)")
    
    In [135]:
    iqr.update_yaxes(showgrid=False, tickformat='$')
    iqr.update_xaxes(showgrid=False, tickformat='$')
    
    In [136]:
    iqr.write_image("iqr.png", width=1200)
    

    Question 4 (Review Analysis): Even though reveiws are subjective, do ceratin neighbourhoods or price ranges reveal any patterns in review sentiment?

    For this we'll use NLTK's Vader Sentiment Intensity Analyzer. Learn more here: https://medium.com/analytics-vidhya/simplifying-social-media-sentiment-analysis-using-vader-in-python-f9e6ec6fc52f

    In [195]:
    import nltk
    nltk.download('vader_lexicon')
    
    # 'normalized, weighted composite score'
    def get_sentiment(sentence):
        
        from nltk.sentiment.vader import SentimentIntensityAnalyzer
        nltk_sentiment = SentimentIntensityAnalyzer()
        score = nltk_sentiment.polarity_scores(sentence)
        return score
    
    [nltk_data] Downloading package vader_lexicon to
    [nltk_data]     /Users/ldugom/nltk_data...
    [nltk_data]   Package vader_lexicon is already up-to-date!
    
    In [279]:
    # make a reviews datframe 
    reviews = (
                rs_d
                    .rename({'id':'review_id'}, axis=1)
                    .merge(ls[['host_id', 'host_name', 'id']], left_on='listing_id', right_on='id')
                    .drop("id", axis=1)
    )
    
    In [276]:
    # attach sentiment scores
    reviews_final = pd.concat([reviews, reviews['comments'].astype(str).apply(get_sentiment).apply(pd.Series)], axis=1)
    
    In [276]:
    # write for later use 
    
    In [139]:
    rs.head()
    
    Out[139]:
    listing_id date
    0 3362 2009-01-21
    1 3362 2009-03-26
    2 3362 2009-07-28
    3 3362 2009-08-27
    4 3362 2009-08-28

    Analyze sentiment scores by host, by neighbourhood, by price range

    In [143]:
    vader_scores = pd.read_csv("/Users/ldugom/sentiment_scores.csv")
    
    In [144]:
    vader_merged = vader_scores.merge(ls, left_on=['listing_id'], right_on=['id'] )
    
    In [145]:
    vader_summary = vader_merged[['listing_id', 'neighbourhood', 'date', 'neg', 'neu', 'pos', 'compound', 'price']]
    

    Using vader's "normalized" score, it looks all neighboourhoods overall have good reviews, with the bottom 10ish neighbourhoods having relatively lower scores, but all in all, still above average (0-1) scores.

    In [146]:
    vader_groupby = vader_summary.groupby("neighbourhood")["compound"].describe().sort_values(["50%", "count"], ascending=False).reset_index()
    vader_groupby.style.background_gradient(cmap="RdYlGn", subset=["25%", "50%", "75%"])
    
    Out[146]:
    neighbourhood count mean std min 25% 50% 75% max
    0 Colonial Village, Shepherd Park, North Portal Estates 1030.000000 0.830860 0.256456 -0.831600 0.826800 0.928450 0.966950 0.997800
    1 Capitol Hill, Lincoln Park 42683.000000 0.819560 0.278853 -0.991200 0.812600 0.923100 0.965500 0.999500
    2 Friendship Heights, American University Park, Tenleytown 5232.000000 0.824316 0.260883 -0.988800 0.807400 0.920850 0.963900 0.999000
    3 North Cleveland Park, Forest Hills, Van Ness 898.000000 0.812953 0.284073 -0.978100 0.804550 0.920250 0.964275 0.996500
    4 Hawthorne, Barnaby Woods, Chevy Chase 2204.000000 0.811768 0.273531 -0.975000 0.796300 0.920050 0.965100 0.998200
    5 Cathedral Heights, McLean Gardens, Glover Park 3653.000000 0.812637 0.278961 -0.977600 0.806600 0.919500 0.963200 0.997900
    6 Howard University, Le Droit Park, Cardozo/Shaw 13732.000000 0.808807 0.285925 -0.991900 0.799575 0.916400 0.961200 0.999200
    7 Near Southeast, Navy Yard 1125.000000 0.816439 0.267780 -0.991000 0.795100 0.916100 0.967000 0.998700
    8 Columbia Heights, Mt. Pleasant, Pleasant Plains, Park View 36651.000000 0.802207 0.291870 -0.990500 0.789700 0.915000 0.961100 0.999000
    9 Union Station, Stanton Park, Kingman Park 45557.000000 0.800956 0.295340 -0.992500 0.790600 0.914400 0.961800 0.999300
    10 Cleveland Park, Woodley Park, Massachusetts Avenue Heights, Woodland-Normanstone Terrace 4119.000000 0.812092 0.265351 -0.991900 0.795500 0.913100 0.960100 0.999100
    11 Dupont Circle, Connecticut Avenue/K Street 27062.000000 0.812291 0.267600 -0.994400 0.795100 0.911500 0.958700 0.998700
    12 North Michigan Park, Michigan Park, University Heights 3354.000000 0.785971 0.318991 -0.980300 0.777800 0.911450 0.961100 0.998500
    13 Kalorama Heights, Adams Morgan, Lanier Heights 13411.000000 0.811656 0.271185 -0.990400 0.795950 0.911400 0.959500 0.999000
    14 Brightwood Park, Crestwood, Petworth 18675.000000 0.796668 0.294904 -0.994200 0.777800 0.909900 0.960600 0.999400
    15 Shaw, Logan Circle 32336.000000 0.800602 0.287426 -0.996500 0.784500 0.909700 0.959300 0.999600
    16 Takoma, Brightwood, Manor Park 5271.000000 0.788944 0.300338 -0.992400 0.770700 0.907500 0.958700 0.998100
    17 Spring Valley, Palisades, Wesley Heights, Foxhall Crescent, Foxhall Village, Georgetown Reservoir 2472.000000 0.783361 0.305192 -0.938100 0.762000 0.906200 0.959525 0.998100
    18 Edgewood, Bloomingdale, Truxton Circle, Eckington 37290.000000 0.779893 0.319276 -0.999000 0.763400 0.906000 0.959000 0.999200
    19 Georgetown, Burleith/Hillandale 12244.000000 0.795383 0.288874 -0.996300 0.777700 0.904200 0.957525 0.998800
    20 Eastland Gardens, Kenilworth 748.000000 0.756188 0.361569 -0.987300 0.750600 0.899900 0.956000 0.999100
    21 Brookland, Brentwood, Langdon 7616.000000 0.769173 0.322171 -0.993200 0.733900 0.899600 0.957100 0.997900
    22 West End, Foggy Bottom, GWU 9908.000000 0.798010 0.269734 -0.992800 0.778300 0.897900 0.951600 0.998500
    23 Douglas, Shipley Terrace 1510.000000 0.770736 0.328035 -0.995300 0.741200 0.896800 0.958075 0.997200
    24 Capitol View, Marshall Heights, Benning Heights 2833.000000 0.752043 0.347429 -0.984300 0.705600 0.895700 0.955800 0.998300
    25 Lamont Riggs, Queens Chapel, Fort Totten, Pleasant Hill 3306.000000 0.769149 0.318053 -0.991900 0.739100 0.893100 0.955900 0.997900
    26 Fairfax Village, Naylor Gardens, Hillcrest, Summit Park 596.000000 0.780133 0.294929 -0.976300 0.753675 0.892650 0.948000 0.997900
    27 Downtown, Chinatown, Penn Quarters, Mount Vernon Square, North Capitol Street 9667.000000 0.765704 0.316928 -0.986400 0.723850 0.890800 0.951000 0.998200
    28 Ivy City, Arboretum, Trinidad, Carver Langston 10915.000000 0.739560 0.352861 -0.996100 0.687200 0.883300 0.951500 0.998500
    29 Southwest Employment Area, Southwest/Waterfront, Fort McNair, Buzzard Point 5484.000000 0.744949 0.334101 -0.992600 0.669700 0.880500 0.947750 0.999300
    30 Mayfair, Hillbrook, Mahaning Heights 1331.000000 0.734551 0.353913 -0.989800 0.678950 0.880300 0.953100 0.995000
    31 Woodridge, Fort Lincoln, Gateway 2277.000000 0.750099 0.322390 -0.991400 0.680800 0.877700 0.948900 0.997500
    32 Historic Anacostia 2381.000000 0.721508 0.362680 -0.982300 0.636900 0.868700 0.949800 0.998500
    33 Congress Heights, Bellevue, Washington Highlands 2640.000000 0.730369 0.329258 -0.988200 0.636900 0.862050 0.940825 0.998900
    34 Twining, Fairlawn, Randle Highlands, Penn Branch, Fort Davis Park, Fort Dupont 3327.000000 0.704898 0.366367 -0.989900 0.624900 0.861900 0.944850 0.998400
    35 Deanwood, Burrville, Grant Park, Lincoln Heights, Fairmont Heights 865.000000 0.732391 0.322536 -0.984400 0.658800 0.861000 0.936000 0.998500
    36 Sheridan, Barry Farm, Buena Vista 1385.000000 0.688582 0.397542 -0.976900 0.624900 0.858800 0.936000 0.997100
    37 River Terrace, Benning, Greenway, Dupont Park 2488.000000 0.694554 0.377851 -0.989200 0.624900 0.851900 0.944000 0.997000
    38 Woodland/Fort Stanton, Garfield Heights, Knox Hill 243.000000 0.719297 0.328980 -0.916800 0.647850 0.843900 0.918450 0.992500
    In [147]:
    import plotly.express as px
    
    # set token
    px.set_mapbox_access_token("pk.eyJ1IjoibGF3cmVuY2VkIiwiYSI6ImNrODFzZnFnNzA0YmczZW9nNWN4aTFvdngifQ.VlB5-L7owXKEXo8JEePk7w")
    fig = px.choropleth_mapbox(vader_groupby, geojson=geojson, color="mean", title="Washington D.C. Neighbourhood Map",
                                   locations="neighbourhood", featureidkey="properties.neighbourhood",opacity=0.5, color_continuous_scale=px.colors.diverging.Geyser,
                               center={"lat": 38.9072, "lon": -77.0369},
                               mapbox_style="light", zoom=10)
    
    fig.update_layout(margin={"r":0,"t":0,"l":0,"b":0})
    fig.show()
    

    Does price range uncover patterns in reviews ?

    In [148]:
    vader_merged.groupby("Price Decile")["compound"].describe().reset_index().sort_values("Price Decile").style.background_gradient(cmap="RdYlGn", subset=["mean"])
    
    Out[148]:
    Price Decile count mean std min 25% 50% 75% max
    0 (-0.001, 53.0] 32654.000000 0.727789 0.348752 -0.999000 0.658800 0.871600 0.946700 0.999200
    1 (53.0, 70.0] 45919.000000 0.767025 0.326381 -0.995300 0.733900 0.897900 0.956200 0.999600
    2 (70.0, 85.0] 48096.000000 0.791076 0.305517 -0.993300 0.778300 0.910100 0.960000 0.999300
    3 (85.0, 99.0] 49222.000000 0.803917 0.292464 -0.996100 0.791875 0.915100 0.961800 0.999400
    4 (99.0, 115.0] 40111.000000 0.802190 0.287598 -0.996500 0.788700 0.910800 0.960000 0.999400
    5 (115.0, 135.0] 49168.000000 0.811322 0.276231 -0.996300 0.796000 0.913500 0.961000 0.999200
    6 (135.0, 165.0] 44409.000000 0.818933 0.269735 -0.991200 0.806700 0.918600 0.962600 0.999100
    7 (165.0, 215.0] 32232.000000 0.798647 0.290408 -0.992800 0.784000 0.909700 0.959500 0.999000
    8 (215.0, 350.0] 26185.000000 0.806802 0.286006 -0.991700 0.790900 0.915900 0.962700 0.999500
    9 (350.0, 10000.0] 10523.000000 0.811074 0.276294 -0.987400 0.800750 0.915100 0.961550 0.998200

    Answer: Price range does not uncover too much besides the fact that the lowest two priced deciles have relatively lower scores.

    In [149]:
    vader_summary['price'].corr(vader_summary['compound'])
    
    Out[149]:
    0.03248190402843484